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Data-Driven Assisted Chance-Constrained Energy and Reserve Scheduling with Wind Curtailment
Case studies performed on the PJM 5-bus and IEEE 118-bus systems demonstrate that the proposed method is capable of accurately accounting the influence of wind curtailment dispatch in CCO.
Systems and Control Systems and Control
Data-driven Optimal Power Flow: A Physics-Informed Machine Learning Approach
This paper proposes a data-driven approach for optimal power flow (OPF) based on the stacked extreme learning machine (SELM) framework.
BIG-bench Machine Learning
Physics-informed machine learning
Fast Calculation of Probabilistic Optimal Power Flow: A Deep Learning Approach
Taking advantage of the deep structure and reconstructive strategy of stacked denoising auto encoders (SDAE), a SDAE-based optimal power flow (OPF) is developed to extract the high-level nonlinear correlations between the system operating condition and the OPF solution.
Fast Calculation of Probabilistic Power Flow: A Model-based Deep Learning Approach
A deep neural network (DNN) is used to approximate the power flow calculation and is trained according to the physical power flow equations to improve its learning ability.
